Module 7 of 13 in the AI Track, a free, self-directed curriculum for building AI that moves revenue.
The idea
This is the daily craft, so go deep. Few-shot, system prompts, role prompting, structured instructions. Chain-of-thought, and when it earns its tokens versus when it just burns them. And schema-forced output, where you make the model return clean JSON, which is what turns it from a toy into a reliable piece of a pipeline.
Then the harder part: context engineering. Deciding what goes in the window, in what order, and what to leave out. Retrieving the right thing instead of stuffing everything in and hoping. On top of that, the money side. Prompt caching, the trade between cost and speed, and knowing when a small fast model does the job better than the big one.
Why it matters
Every node of a production outreach engine is a prompt running under load. The gap between a demo that works once and a system that runs thousands of times without falling over is almost all this skill. It’s also the most portable thing in the track: it pays off on day one, in whatever you’re already building.
Build this
A reusable prompt that takes a raw company description and returns a structured, personalized outreach angle. Not a toy. An actual node you’d run over and over in a real pipeline.
How to work through it
- Anthropic’s prompt engineering guide and cookbook
- The OpenAI cookbook patterns
- DeepLearning.AI’s prompt engineering course
- Build and test the schema-forced outreach-angle prompt